LEAST: 'Local' text-conditioned image style transfer

Autor:	Singh, Silky, Jandial, Surgan, Shahid, Simra, Java, Abhinav
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Text-conditioned style transfer enables users to communicate their desired artistic styles through text descriptions, offering a new and expressive means of achieving stylization. In this work, we evaluate the text-conditioned image editing and style transfer techniques on their fine-grained understanding of user prompts for precise "local" style transfer. We find that current methods fail to accomplish localized style transfers effectively, either failing to localize style transfer to certain regions in the image, or distorting the content and structure of the input image. To this end, we carefully design an end-to-end pipeline that guarantees local style transfer according to users' intent. Further, we substantiate the effectiveness of our approach through quantitative and qualitative analysis. The project code is available at: https://github.com/silky1708/local-style-transfer. Comment: Accepted to AI for Content Creation (AI4CC) Workshop at CVPR 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.16330 Zobrazit plný text záznamu View this record from Arxiv